[R] calculating likelihood-based CI

[Prof Brian D Ripley]

On Wed, 15 May 2002, vito muggeo wrote:

> Hi there,
> I'm interested in estimating likelihood- (or simply deviance- for GLM) based
> CI. I use the following code, but is there a more efficient way to do it?
>
> obj<-glm(y~x+z....) #fit the full model
> beta.z<-seq(a, b, length=500) #where a<coef(obj)["z"]<b
> out<-list()
> for(i in 1:500){
> out[[i]]<-update(obj,.~.-z+offset(I(beta.z[i]*z)) )  }
> dev.z<-sapply(out, function(x)x$dev) #profile dev for \beta_z
> range(beta.z[(dev.z-obj$dev)<3.8]) #likelihood based CI
>
>
> I understand that there could be some package to do it (for instance in
> MASS???), but I like know the code to use it with some nonstandard
> situations.

Look at how the confint and profile methods for glm in MASS work.
They are similar, but

1) work at glm.fit level
2) use spline interpolation

>
> best,
> vito
>
>
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-- 
Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
1 South Parks Road,                     +44 1865 272860 (secr)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595

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